Cardiac magnetic resonance (CMR) imaging and computed tomography (CT) are two common non-invasive imaging methods for assessing patients with cardiovascular disease. CMR typically acquires multiple sparse 2D slices, with unavoidable respiratory motion artefacts between slices, whereas CT acquires isotropic dense data but uses ionising radiation. In this study, we explore the combination of Slice Shifting Algorithm (SSA), Spatial Transformer Network (STN), and Label Transformer Network (LTN) to: 1) correct respiratory motion between segmented slices, and 2) transform sparse segmentation data into dense segmentation. All combinations were validated using synthetic motion-corrupted CMR slice segmentation generated from CT in 1699 cases, where the dense CT serves as the ground truth. In 199 testing cases, SSA-LTN achieved the best results for Dice score and Huasdorff distance (94.0% and 4.7 mm respectively, average over 5 labels) but gave topological errors in 8 cases. STN was effective as a plug-in tool for correcting all topological errors with minimal impact on overall performance (93.5% and 5.0 mm respectively). SSA also proves to be a valuable plug-in tool, enhancing performance over both STN-based and LTN-based models. The code for these different combinations is available at https://github.com/XESchong/STACOM2024.
Karli Gillette, Matthias A. F. Gsell, Claudia Nagel et al.
Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.
Temesgen Mehari, Ashish Sundar, Alen Bosnjakovic et al.
Feature importance methods promise to provide a ranking of features according to importance for a given classification task. A wide range of methods exist but their rankings often disagree and they are inherently difficult to evaluate due to a lack of ground truth beyond synthetic datasets. In this work, we put feature importance methods to the test on real-world data in the domain of cardiology, where we try to distinguish three specific pathologies from healthy subjects based on ECG features comparing to features used in cardiologists' decision rules as ground truth. Some methods generally performed well and others performed poorly, while some methods did well on some but not all of the problems considered.
Cardiac Magnetic Resonance (CMR) imaging is widely used for heart modelling and digital twin computational analysis due to its ability to visualize soft tissues and capture dynamic functions. However, the anisotropic nature of CMR images, characterized by large inter-slice distances and misalignments from cardiac motion, poses significant challenges to accurate model reconstruction. These limitations result in data loss and measurement inaccuracies, hindering the capture of detailed anatomical structures. This study introduces MorphiNet, a novel network that enhances heart model reconstruction by leveraging high-resolution Computer Tomography (CT) images, unpaired with CMR images, to learn heart anatomy. MorphiNet encodes anatomical structures as gradient fields, transforming template meshes into patient-specific geometries. A multi-layer graph subdivision network refines these geometries while maintaining dense point correspondence. The proposed method achieves high anatomy fidelity, demonstrating approximately 40% higher Dice scores, half the Hausdorff distance, and around 3 mm average surface error compared to state-of-the-art methods. MorphiNet delivers superior results with greater inference efficiency. This approach represents a significant advancement in addressing the challenges of CMR-based heart model reconstruction, potentially improving digital twin computational analyses of cardiac structure and functions.